Tricog Health’s target of conducting 100 million cardiac screenings by 2030 is more than a growth ambition for a Bengaluru healthtech company. It is also a test of whether India can use connected diagnostics to shift cardiac care closer to primary healthcare centres, clinics and nursing homes, where patients first enter the health system.
The company says it has already screened more than 41 million patients and currently screens over a million people globally every month. Its stated expansion model combines an AI-enabled electrocardiogram device, encrypted cloud transmission and specialist review. The model is designed to address a basic weakness in India’s healthcare geography: patients may be able to reach a local facility, but the facility may not have a cardiologist or the capability to interpret a cardiac test quickly.
That gap becomes especially important for conditions in which time affects treatment. Tricog Founder and Chief Executive Officer Dr Charit Bhograj told the Press Trust of India that the company’s hub-and-spoke model had reduced treatment delays that previously averaged 360 minutes in urban India to under 30 minutes. The supplied report does not provide the methodology, locations or time period behind that comparison, but the claimed reduction illustrates the operational problem the company is trying to solve: diagnosis is not only a clinical question, but also a question of referral, communication and access to the next level of care.
The company began public-health partnerships with the Goa government in 2018 and has since expanded to Maharashtra, Telangana, Odisha, Bihar, Karnataka and Manipur, according to the report. It also operates in 14 countries in the Global South, including the Philippines, Malaysia and Kenya. The geography is significant because the model is being presented not as a service limited to metropolitan hospitals, but as a network connecting community-level facilities with tertiary-care institutions.
The central infrastructure challenge is the distribution of expertise. India has about 6,500 cardiologists and 50,000 physicians, according to estimates cited by Bhograj. The report also cites expert estimates that 3.1 million people die from cardiovascular diseases in the country, with 50 per cent of these deaths potentially preventable through early identification and treatment. These figures are attributed in the source report; it does not identify the underlying datasets or explain how the estimates were calculated.
Even with those limitations, the direction of the problem is clear from the evidence supplied. A relatively limited specialist base has to serve a large population with substantial cardiovascular risk. Centralising diagnosis in major hospitals can create delays for patients who live far from specialist facilities or who enter the system through a primary health centre. A connected diagnostic model attempts to separate the location of the test from the location of specialist interpretation.
## From a local test to a distributed care network
Tricog’s VCardia device is deployed at primary healthcare centres, clinics, nursing homes and hospitals. The company says the device records an ECG, encrypts the data and sends it to the cloud, where an algorithm identifies more than 140 heart conditions. A team of 120 doctors then reviews every AI diagnosis and returns a final report within six minutes, Bhograj said.
This structure is important because the technology is not described as replacing doctors. Instead, it is positioned as a screening and triage layer connecting a point of care to a larger clinical network. The algorithm expands the ability of a local facility to process cardiac data, while the doctor review is intended to provide a human clinical check before the report is finalised.
That division of labour also reveals the limits of the technology-led narrative. The device and algorithm can support detection, but they do not by themselves create an ambulance network, a referral pathway, an available hospital bed or an affordable treatment option. The health system still needs the institutions and transport links that allow a patient identified at a community facility to reach appropriate care.
The company’s reported shift from an average 360-minute treatment delay to less than 30 minutes is therefore presented as the outcome of a hub-and-spoke arrangement, not simply of an AI tool. Community outreach clinics function as the spokes, while tertiary-care hospitals act as hubs. The value of the system depends on the connection between them: data must move quickly, doctors must review it, and patients must be directed into treatment.
For cities, this matters even when the facilities involved are not in large urban centres. Urban healthcare systems often contain high-end hospitals alongside primary facilities with fewer specialists and less diagnostic capacity. Delays can arise from fragmentation between these levels. A connected network may help reduce one layer of fragmentation, although the report does not establish how consistently the model performs across states, facility types or patient groups.
## Public partnerships are the operating layer
The company’s work with state governments indicates that scaling cardiac screening is not only a private-sector technology exercise. Tricog’s first public-health partnership was with Goa in 2018, followed by expansion to six other Indian states named in the report. The Goa government and Tricog recently organised a National Cardiovascular Disease Summit involving government officials, clinicians, public-health leaders and implementation teams.
The institutional question is how such systems are integrated into existing public-health responsibilities. Primary healthcare centres are often the first formal point of contact for residents outside major hospitals. If screening is added without a defined referral and follow-up system, the result may be more diagnoses without enough capacity to treat them. If the system is integrated with tertiary hospitals and local outreach, screening can become part of a pathway rather than a standalone test.
The report does not provide details on the financing of the state partnerships, procurement arrangements, data-governance framework, patient consent process or performance audits. Those details will matter as the company moves from 41 million screenings towards its 2030 target. They will determine whether the model can be sustained through public budgets and replicated across different state health systems.
Data governance is another institutional layer. The company says patient data is encrypted and transmitted to the cloud. That describes a security measure, but the supplied material does not explain where data is stored, who can access it, how long it is retained or how patients can exercise control over it. These questions become more consequential as diagnostic systems expand across multiple states and countries.
The six-minute reporting timeline also requires interpretation. It refers to the time taken to return a final report after the AI-assisted process and doctor review, according to Bhograj. It does not necessarily represent the full time from a patient’s arrival to treatment. The difference between report generation and treatment access is precisely why the hub-and-spoke model, referral mechanisms and hospital capacity remain central to the story.
## The scale target and what it does not establish
A target of 100 million screenings by 2030 would represent a major expansion from the more than 41 million screenings the company says it has completed. The company also says it is currently screening over a million patients globally every month. These numbers indicate significant existing scale, but they do not by themselves establish how many screenings led to confirmed diagnoses, how many patients entered treatment, or how outcomes differed between locations.
Screening volumes are an important measure of reach. They show whether technology is being placed in facilities that people can access. But volume alone cannot answer whether the system is improving population health. For that, policymakers and health administrators would also need information on false positives and false negatives, referral completion, treatment initiation, follow-up and patient outcomes. None of those measures is provided in the supplied report.
The company’s expansion also takes place against a serious risk profile. Bhograj said cardiovascular disease is projected to remain India’s single largest killer well beyond 2050 and that one in five heart attacks occurs before the age of 45. These claims are attributed to him in the report and are not supported there by additional source details. If younger patients are increasingly affected, cardiac care cannot be planned only around tertiary hospitals serving older populations. Primary-level awareness, accessible screening and rapid referral become more important parts of the urban and regional health system.
At the same time, the reported numbers should not be read as proof that technology alone can overcome the specialist shortage. The model still relies on a team of 120 doctors to review AI diagnoses. As deployment grows, the capacity of that clinical review layer, the consistency of protocols and the availability of treatment facilities will become as important as the number of devices installed.
The evidence therefore points to a distributed-care model with three linked components: local access to diagnostic equipment, remote or centralised specialist interpretation, and a functioning referral connection to hospitals. Weakness in any one component can reduce the value of the others. A screening device without referral capacity may identify risk without resolving it. A specialist network without local access may remain concentrated in large hospitals. A hospital network without timely diagnostic information may continue to lose time before treatment.
## The larger urban health question
Tricog’s expansion raises a broader question about how cities and states should organise specialised healthcare in an era of uneven urban growth. India’s healthcare infrastructure is not divided neatly between urban and rural systems. Peripheral settlements, smaller cities and metropolitan outskirts often depend on a mix of public facilities, private clinics, nursing homes and tertiary hospitals. The challenge is to make those parts work as a network rather than as isolated points of care.
Connected diagnostics can contribute to that network by moving information faster than patients may be able to move. But the evidence supplied here supports a more measured conclusion than a simple technology success story. The reported reduction in treatment delays is linked to the company’s hub-and-spoke model, while the long-term public value of the system will depend on verified outcomes, transparent institutional arrangements and the capacity of health systems to absorb patients identified through screening.
The 2030 target will therefore be meaningful not only as a measure of how many tests are conducted, but also as an opportunity to assess what follows each test. The next developments to monitor are the company’s expansion across state health systems, the durability of its government partnerships, the operation of its clinical review network and the evidence connecting screening to timely treatment and improved outcomes. Those details will determine whether cardiac screening in India becomes a larger diagnostic market or a genuinely more accessible layer of public health infrastructure.
